Facial recognition in PCs, tablets and phones is very dependent on image quality produced by integrated user-facing cameras, which in turn are very dependent on ambient lighting conditions. These cameras are primarily calibrated to tune their settings for best photographic quality as opposed to focusing on the user's face. As a result in sub-optimal lighting conditions, e.g. dark room, direct sunlight, bright background (e.g., open window), the user's face is too dark, too bright or silhouetted. This severely hampers face detection and recognition, thus making facial recognition an unreliable experience across typical usage environments.
Current facial recognition solutions suffer from degraded performance and, in some cases, fail to even detect the user's face in adverse lighting conditions, such as when the illumination is less than 30 lux (dark areas), greater than 10,000 lux (direct sunlight) or obscured by a bright background (e.g., when the foreground illumination is less than 100 lux and the background is greater than 1000 lux). Webcams perform unevenly across platforms and OEMs and are not calibrated to highlight the face over the background in adverse lighting conditions, leading to the failure of face recognition.
A common technique used to compensate for low light is to convert the laptop/tablet screen to an all-white image, thus using the screen's brightness to illuminate the subject (e.g. Sensible Vision's FaceBright). However, tablet/notebook screen brightness is frequently auto-tuned by ambient light sensors to a low setting in low lighting conditions, resulting in the white screen not being very bright and hence hampering the efficacy of this method. Distance of face from the computer screen is another factor contributing to this method's questionable reliability. Also this method does not address the problems inherent from direct sunlight and from bright backgrounds.